Overview

Dataset statistics

Number of variables18
Number of observations207260
Missing cells913021
Missing cells (%)24.5%
Total size in memory28.5 MiB
Average record size in memory144.0 B

Variable types

Text17
Numeric1

Alerts

Sub-product has 43015 (20.8%) missing valuesMissing
Sub-issue has 196913 (95.0%) missing valuesMissing
Consumer complaint narrative has 177869 (85.8%) missing valuesMissing
Company public response has 148802 (71.8%) missing valuesMissing
State has 2194 (1.1%) missing valuesMissing
ZIP code has 9286 (4.5%) missing valuesMissing
Tags has 178995 (86.4%) missing valuesMissing
Consumer consent provided? has 155947 (75.2%) missing valuesMissing
Complaint ID has unique valuesUnique

Reproduction

Analysis started2023-11-05 04:56:57.389685
Analysis finished2023-11-05 04:57:15.813981
Duration18.42 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct1947
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
2023-11-05T00:57:17.161882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters2072600
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row2016-10-26
2nd row2015-03-27
3rd row2015-04-20
4th row2013-04-29
5th row2013-05-29
ValueCountFrequency (%)
2012-05-15 380
 
0.2%
2012-05-17 379
 
0.2%
2013-02-07 348
 
0.2%
2012-05-31 326
 
0.2%
2014-06-26 325
 
0.2%
2012-06-04 323
 
0.2%
2013-01-08 321
 
0.2%
2013-01-16 317
 
0.2%
2015-08-26 312
 
0.2%
2015-08-27 312
 
0.2%
Other values (1937) 203917
98.4%
2023-11-05T00:57:19.015951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 466051
22.5%
- 414520
20.0%
1 381380
18.4%
2 367639
17.7%
3 94011
 
4.5%
6 79070
 
3.8%
5 74114
 
3.6%
4 72451
 
3.5%
7 48016
 
2.3%
8 37960
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1658080
80.0%
Dash Punctuation 414520
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 466051
28.1%
1 381380
23.0%
2 367639
22.2%
3 94011
 
5.7%
6 79070
 
4.8%
5 74114
 
4.5%
4 72451
 
4.4%
7 48016
 
2.9%
8 37960
 
2.3%
9 37388
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
- 414520
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2072600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 466051
22.5%
- 414520
20.0%
1 381380
18.4%
2 367639
17.7%
3 94011
 
4.5%
6 79070
 
3.8%
5 74114
 
3.6%
4 72451
 
3.5%
7 48016
 
2.3%
8 37960
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2072600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 466051
22.5%
- 414520
20.0%
1 381380
18.4%
2 367639
17.7%
3 94011
 
4.5%
6 79070
 
3.8%
5 74114
 
3.6%
4 72451
 
3.5%
7 48016
 
2.3%
8 37960
 
1.8%
Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
2023-11-05T00:57:19.562109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length16
Mean length12.37997684
Min length8

Characters and Unicode

Total characters2565874
Distinct characters30
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMoney transfers
2nd rowBank account or service
3rd rowBank account or service
4th rowMortgage
5th rowMortgage
ValueCountFrequency (%)
mortgage 101680
25.3%
service 44826
11.1%
account 44594
11.1%
or 44594
11.1%
bank 44594
11.1%
credit 42932
10.7%
card 42826
10.6%
loan 8457
 
2.1%
debt 7861
 
2.0%
collection 7861
 
2.0%
Other values (9) 12035
 
3.0%
2023-11-05T00:57:20.659230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 286453
11.2%
e 261706
10.2%
a 244285
9.5%
o 222165
8.7%
t 212502
8.3%
g 204101
 
8.0%
195000
 
7.6%
c 192794
 
7.5%
n 116823
 
4.6%
M 102549
 
4.0%
Other values (20) 527496
20.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2158106
84.1%
Uppercase Letter 212768
 
8.3%
Space Separator 195000
 
7.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 286453
13.3%
e 261706
12.1%
a 244285
11.3%
o 222165
10.3%
t 212502
9.8%
g 204101
9.5%
c 192794
8.9%
n 116823
5.4%
i 97459
 
4.5%
d 89342
 
4.1%
Other values (11) 230476
10.7%
Uppercase Letter
ValueCountFrequency (%)
M 102549
48.2%
C 48440
22.8%
B 44594
21.0%
D 7861
 
3.7%
L 5508
 
2.6%
S 2866
 
1.3%
P 718
 
0.3%
O 232
 
0.1%
Space Separator
ValueCountFrequency (%)
195000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2370874
92.4%
Common 195000
 
7.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 286453
12.1%
e 261706
11.0%
a 244285
10.3%
o 222165
9.4%
t 212502
9.0%
g 204101
8.6%
c 192794
8.1%
n 116823
 
4.9%
M 102549
 
4.3%
i 97459
 
4.1%
Other values (19) 430037
18.1%
Common
ValueCountFrequency (%)
195000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2565874
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 286453
11.2%
e 261706
10.2%
a 244285
9.5%
o 222165
8.7%
t 212502
8.3%
g 204101
 
8.0%
195000
 
7.6%
c 192794
 
7.5%
n 116823
 
4.6%
M 102549
 
4.0%
Other values (20) 527496
20.6%

Sub-product
Text

MISSING 

Distinct47
Distinct (%)< 0.1%
Missing43015
Missing (%)20.8%
Memory size1.6 MiB
2023-11-05T00:57:21.287697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length34
Mean length19.67125331
Min length4

Characters and Unicode

Total characters3230905
Distinct characters51
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowInternational money transfer
2nd rowOther bank product/service
3rd rowOther bank product/service
4th rowConventional fixed mortgage
5th rowOther mortgage
ValueCountFrequency (%)
mortgage 97024
22.6%
other 54314
12.6%
conventional 37065
 
8.6%
account 34766
 
8.1%
checking 31880
 
7.4%
fixed 27105
 
6.3%
loan 13435
 
3.1%
fha 11576
 
2.7%
credit 10628
 
2.5%
adjustable 9960
 
2.3%
Other values (76) 101732
23.7%
2023-11-05T00:57:22.459703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 337909
 
10.5%
t 278091
 
8.6%
265240
 
8.2%
o 259520
 
8.0%
a 229841
 
7.1%
g 229045
 
7.1%
n 226430
 
7.0%
r 198742
 
6.2%
i 141085
 
4.4%
c 139260
 
4.3%
Other values (41) 925742
28.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2698848
83.5%
Space Separator 265240
 
8.2%
Uppercase Letter 222693
 
6.9%
Other Punctuation 15135
 
0.5%
Close Punctuation 12992
 
0.4%
Open Punctuation 12992
 
0.4%
Dash Punctuation 2927
 
0.1%
Final Punctuation 78
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 337909
12.5%
t 278091
10.3%
o 259520
9.6%
a 229841
8.5%
g 229045
8.5%
n 226430
8.4%
r 198742
 
7.4%
i 141085
 
5.2%
c 139260
 
5.2%
m 104601
 
3.9%
Other values (15) 554324
20.5%
Uppercase Letter
ValueCountFrequency (%)
C 76109
34.2%
O 54314
24.4%
A 23704
 
10.6%
H 17428
 
7.8%
F 11717
 
5.3%
M 11195
 
5.0%
R 10497
 
4.7%
V 5280
 
2.4%
S 3290
 
1.5%
N 2927
 
1.3%
Other values (8) 6232
 
2.8%
Other Punctuation
ValueCountFrequency (%)
/ 8565
56.6%
. 3942
26.0%
, 2628
 
17.4%
Space Separator
ValueCountFrequency (%)
265240
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12992
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12992
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2927
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 78
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2921541
90.4%
Common 309364
 
9.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 337909
11.6%
t 278091
 
9.5%
o 259520
 
8.9%
a 229841
 
7.9%
g 229045
 
7.8%
n 226430
 
7.8%
r 198742
 
6.8%
i 141085
 
4.8%
c 139260
 
4.8%
m 104601
 
3.6%
Other values (33) 777017
26.6%
Common
ValueCountFrequency (%)
265240
85.7%
) 12992
 
4.2%
( 12992
 
4.2%
/ 8565
 
2.8%
. 3942
 
1.3%
- 2927
 
0.9%
, 2628
 
0.8%
’ 78
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3230827
> 99.9%
Punctuation 78
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 337909
 
10.5%
t 278091
 
8.6%
265240
 
8.2%
o 259520
 
8.0%
a 229841
 
7.1%
g 229045
 
7.1%
n 226430
 
7.0%
r 198742
 
6.2%
i 141085
 
4.4%
c 139260
 
4.3%
Other values (40) 925664
28.7%
Punctuation
ValueCountFrequency (%)
’ 78
100.0%

Issue
Text

Distinct93
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
2023-11-05T00:57:23.220310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length39
Mean length32.87727975
Min length4

Characters and Unicode

Total characters6814145
Distinct characters47
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowOther transaction issues
2nd rowAccount opening, closing, or management
3rd rowMaking/receiving payments, sending money
4th rowApplication, originator, mortgage broker
5th rowLoan modification,collection,foreclosure
ValueCountFrequency (%)
loan 90816
 
12.6%
modification,collection,foreclosure 58940
 
8.2%
account 51239
 
7.1%
or 32681
 
4.5%
payments 30427
 
4.2%
servicing 26608
 
3.7%
escrow 26608
 
3.7%
opening 19948
 
2.8%
closing 19948
 
2.8%
management 19762
 
2.7%
Other values (183) 345730
47.8%
2023-11-05T00:57:24.742684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 729404
 
10.7%
n 584175
 
8.6%
e 558059
 
8.2%
i 521890
 
7.7%
515447
 
7.6%
c 482780
 
7.1%
t 415969
 
6.1%
a 401415
 
5.9%
r 350149
 
5.1%
s 306261
 
4.5%
Other values (37) 1948596
28.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5803627
85.2%
Space Separator 515447
 
7.6%
Other Punctuation 255908
 
3.8%
Uppercase Letter 239163
 
3.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 729404
12.6%
n 584175
10.1%
e 558059
9.6%
i 521890
9.0%
c 482780
8.3%
t 415969
 
7.2%
a 401415
 
6.9%
r 350149
 
6.0%
s 306261
 
5.3%
l 294187
 
5.1%
Other values (15) 1159338
20.0%
Uppercase Letter
ValueCountFrequency (%)
L 87366
36.5%
A 35479
14.8%
C 22906
 
9.6%
D 17783
 
7.4%
P 11221
 
4.7%
M 10468
 
4.4%
B 9475
 
4.0%
O 8129
 
3.4%
U 6832
 
2.9%
T 5877
 
2.5%
Other values (7) 23627
 
9.9%
Other Punctuation
ValueCountFrequency (%)
, 230059
89.9%
/ 22185
 
8.7%
' 3455
 
1.4%
. 209
 
0.1%
Space Separator
ValueCountFrequency (%)
515447
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6042790
88.7%
Common 771355
 
11.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 729404
12.1%
n 584175
9.7%
e 558059
 
9.2%
i 521890
 
8.6%
c 482780
 
8.0%
t 415969
 
6.9%
a 401415
 
6.6%
r 350149
 
5.8%
s 306261
 
5.1%
l 294187
 
4.9%
Other values (32) 1398501
23.1%
Common
ValueCountFrequency (%)
515447
66.8%
, 230059
29.8%
/ 22185
 
2.9%
' 3455
 
0.4%
. 209
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6814145
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 729404
 
10.7%
n 584175
 
8.6%
e 558059
 
8.2%
i 521890
 
7.7%
515447
 
7.6%
c 482780
 
7.1%
t 415969
 
6.1%
a 401415
 
5.9%
r 350149
 
5.1%
s 306261
 
4.5%
Other values (37) 1948596
28.6%

Sub-issue
Text

MISSING 

Distinct57
Distinct (%)0.6%
Missing196913
Missing (%)95.0%
Memory size1.6 MiB
2023-11-05T00:57:25.375732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length37
Mean length28.6804871
Min length13

Characters and Unicode

Total characters296757
Distinct characters47
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowDebt is not mine
2nd rowNot given enough info to verify debt
3rd rowFrequent or repeated calls
4th rowNot given enough info to verify debt
5th rowAccount status
ValueCountFrequency (%)
debt 4692
 
9.1%
not 3627
 
7.0%
to 3491
 
6.7%
info 1509
 
2.9%
enough 1493
 
2.9%
verify 1493
 
2.9%
given 1493
 
2.9%
is 1465
 
2.8%
mine 1465
 
2.8%
or 1142
 
2.2%
Other values (164) 29911
57.8%
2023-11-05T00:57:26.479887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
41434
14.0%
e 34734
11.7%
t 30794
 
10.4%
o 21830
 
7.4%
n 18712
 
6.3%
a 15429
 
5.2%
i 15349
 
5.2%
r 12381
 
4.2%
d 11106
 
3.7%
s 9405
 
3.2%
Other values (37) 85583
28.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 242700
81.8%
Space Separator 41434
 
14.0%
Uppercase Letter 10865
 
3.7%
Other Punctuation 1560
 
0.5%
Decimal Number 132
 
< 0.1%
Dash Punctuation 66
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 34734
14.3%
t 30794
12.7%
o 21830
 
9.0%
n 18712
 
7.7%
a 15429
 
6.4%
i 15349
 
6.3%
r 12381
 
5.1%
d 11106
 
4.6%
s 9405
 
3.9%
l 8120
 
3.3%
Other values (16) 64840
26.7%
Uppercase Letter
ValueCountFrequency (%)
D 2771
25.5%
N 1678
15.4%
C 1333
12.3%
T 1215
11.2%
A 1171
10.8%
F 1071
 
9.9%
R 558
 
5.1%
I 420
 
3.9%
S 282
 
2.6%
H 187
 
1.7%
Other values (5) 179
 
1.6%
Other Punctuation
ValueCountFrequency (%)
' 893
57.2%
/ 667
42.8%
Decimal Number
ValueCountFrequency (%)
8 66
50.0%
9 66
50.0%
Space Separator
ValueCountFrequency (%)
41434
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 66
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 253565
85.4%
Common 43192
 
14.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 34734
13.7%
t 30794
12.1%
o 21830
 
8.6%
n 18712
 
7.4%
a 15429
 
6.1%
i 15349
 
6.1%
r 12381
 
4.9%
d 11106
 
4.4%
s 9405
 
3.7%
l 8120
 
3.2%
Other values (31) 75705
29.9%
Common
ValueCountFrequency (%)
41434
95.9%
' 893
 
2.1%
/ 667
 
1.5%
8 66
 
0.2%
- 66
 
0.2%
9 66
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 296757
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
41434
14.0%
e 34734
11.7%
t 30794
 
10.4%
o 21830
 
7.4%
n 18712
 
6.3%
a 15429
 
5.2%
i 15349
 
5.2%
r 12381
 
4.2%
d 11106
 
3.7%
s 9405
 
3.2%
Other values (37) 85583
28.8%
Distinct29345
Distinct (%)99.8%
Missing177869
Missing (%)85.8%
Memory size1.6 MiB
2023-11-05T00:57:27.214590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5151
Median length3082
Mean length1298.24021
Min length13

Characters and Unicode

Total characters38156578
Distinct characters96
Distinct categories13 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29303 ?
Unique (%)99.7%

Sample

1st rowTo whom it concerns, I would like to file a formal complaint against Citi Prepaid Services, regarding an outbound manual money transfer made on XX/XX/2016. The transfer was never delivered to my partners XXXX account with XXXX, and also was n't delivered to the intermediary bank, XXXX XXXX. We have been told by Citi Prepaid that the transfer left our account and should have been received on XX/XX/2016. Over a month ago Citi Prepaid informed us it was being elevated to their own personal investigations department and that we would hear from them regarding the status of our investigation. During this time frame we have heard nothing aside from that it is being investigated. We have been given zero updates with no regard for any questions we have risen concerning the status of this ongoing investigation. This is highly concerning as now we are worried that the money is lost or stolen and we have no support from Citi Prepaid Services in tracking the missing transfer. Their costumer services supervisor, XXXX, Informed us they can not give us a tracking number or any information, which seems unbelievable and suspicious. The effects of this situation go beyond just our missing money. My partner and I are damaging our credit ratings by the day, and due to a total lack of confidence in Citi Prepaid 's transfer system we are afraid to move more money into our personal accounts. I request that you respond to this compliant urgently. Yours Sincerely,
2nd rowMy name is XXXX XXXX XXXX and huband name is XXXX XXXX XXXX I am filing complaint regarding a finanical company, name OneMain Finanical XXXX address XXXX XXXX XXXX, XXXX XXXX XXXX, XXXX, TX XXXX their phone number XXXX they have an account which they are telling us it our account the account number is XXXX open XX/XX/XXXX, joint account installment account loan type is unserured. I have a company name XXXX XXXX which is helping us and they have send letters on our behalf. The company information is ( C ) XXXX XXXX XXXX ( R ). All rights reserved. XXXX XXXX XXXX, Attorney at Law, XXXX XXXX XXXX XXXX, and of counsel attorneys. XXXX XXXX XXXX XXXX, XXXX XXXX XXXX Utah, XXXX of Use Privacy Policy Email Preferences. Credit Access. Attorney XXXX, phone number XXXX. My email is how they find our information and verified is I who is calling my husband have his own account with the XXXX XXXX XXXX, it with my email. XXXXXXXXXXXX my address XXXX XXXX XXXX XXXX, XXXX XXXX, PA XXXX, my phone number is XXXX, birthday XX/XX/XXXX. My complaint is that this company is accusing us of owning XXXX the high balance is {$17000.00} from XX/XX/XXXX to XX/XX/XXXX pay status is a charge off the account number they have on file is XXXX. XXXX XXXX have wrote letters at least every sixty day since XX/XX/XXXX. They have send us letters refusing to take this the credit report off our credit bureaus, because they have tell me that all XXXX credit reporting agencies have the same information. Now from my understanding it is the company that report to these credit agencies, XXXX, XXXX and XXXX so that is not correct statement since, OneMain Financial reported this information to the credit agencies. I truely do not know this company and never dealt with them that I am aware of. I and my husband need help under credit laws that said this have to be an accurate reporting and this company have not given correct information. The XXXX Lax company have did every thing under law to help us. Please help us. Thank You for whatever you can do to bring the truth to light.
3rd rowXXXX 2015 : I called to make a payment on XXXX, asked the rep directly if they could waive any of the late fees from previous months since I was catching up from being XXXX payment behind. In previous calls trying to catch up I asked if I had late fees to pay and reps at times said no. What they really meant was you do n't have to pay them now. Slightly inaccurate when a customer ( me ) asks what is my late fee or when does it apply. Last XXXX XXXX, I called to make a payment ; the young lady said late fees go into effect on the XXXX so I could have a manager call me back. Wrong! Yes a manager could call me back but a late fee was applied to my account {$28.00} for waiting to hear back from a manager. I still offered to make a payment Thursday the XXXX so it 's Super upsetting to incur any fee for something you ask about directly and are misinformed about. She was wrong about the late feeShe was wrong about having a manger call me back. Someone called saying they were from US Bank and left vm with general XXXX numbers and no name. so I had to call back XXXX and start the request for manager to call back all over again. This is all happening so fast my XXXX credit rating has slipped below XXXX. Thanks US Bank for making it so difficult to get accurate information about payment and fees information. Your reps are wildly inconsistent. Are they trying to drive customers into further default? Is US Bank trying to cause consumers to default on home mortgages and they foreclose on them? I 'm back to work as will be paid in full as of XX/XX/2015. How about the rest of their customers who are n't as fortunate and have to go through the repeated misinformation and delays associated with trying to get right information and make right amounts of payments to get out from default. God bless ... Please advise ... XXXX XXXX
4th rowMortgage Fraud, defective title, defective mortgage, inflated appraisal. The mortgage companies appraiser partially retired, showed up extremely intoxicated falling on our side walk gashing his head before the appraisal took place, after providing him with a wash cloth, the appraisal was cut short and not complete or accurate. Mortgage was bought and sold without my knowledge several times each time the amounts owed would be different, I have kept the documents through the years. Over a year ago it came to my attention that the title was defective, the house is on the middle lot which is not listed or a on the mortgage. I notified the mortgage company of this error and after no response I filed an motion in court to have the defective mortgage dismissed, the mortgage company did not follow through with this. Now more than a year later they have filed in court to have the mortgage reformed and for foreclosure, I lost my job of 20 years and have not been able to find on since and am very behind in payments. The forclosure notice was not served the mortgage company has asked for the court proceedings to be put on the fast track which denies a trial and evidence or witnesses, this proceeding is complex with many documents of evidence and facts not appropriate for the fast track " XXXX '' also known as the fast track to injustice as your right to a trial and to be heard are denied, justice denied. The mortgage is defective, it was brought to their attention over a year ago. The appraisal was inflated, the chain of command of the title is fraudulent and payments and insurance payments were not properly applied throughout the years. The mortgage company never paid the taxes on the middle lot as they did on the other XXXX lots, they only paid the taxes on the XXXX lots listed on the mortgage. Violation of Unfair and Deceptive Trade Practices Act. a ) Failing to promptly and/or properly pay taxes or insurance premiums when due, so that the maximum tax discount available to Defendants could be obtained on Defendants ' property and so that insurance coverage on the property would not lapse. b ) Failing to provide Defendants with an annual statement of the escrow account kept for payment of taxes and insurance. Charging excessive fees and making payments of fees to parties not entitled to receive them. TILA Rescission. The mortgage and note which are the subject of this action have been rescinded and therefore the mortgage ( s ) and note ( s ) are void. Unclean Hand. Plaintiff has unclean hands due to its actions described below and therefore is prohibited from obtaining equitable relief of foreclosure. As a matter of equity, this Court should refuse to foreclose this mortgage because acceleration of this note would be inequitable, unjust, and unconscionable. Plaintiff has waived the right to acceleration due to intentionally misleading and reckless conduct for which it is liable Lack of Jurisdiction. It appears on the face of the complaint that a person other than the Plaintiff was the true owner of the claim sued upon at the time this action was filed and that the Plaintiff is not the real party in interest and is not shown to be authorized to bring this foreclosure action. ( middle lot with house is still in my name only and not listed on the mortgage ) I was never served the documents : Failure to Provide FDCPA Notice. Plaintiff brought this action without providing notice to Defendant of Defendant 's right to dispute the debt, pursuant to the Fair Debt Collection Practices Act. As indicated in the Notice attached to the Complaint, filed XX/XX/XXXX, but not served upon Defendant until XX/XX/XXXX. Plaintiff is required to notify Defendant, pursuant to 15 U.S.C 1601, et seq., that Defendant may dispute the debt and Plaintiff is required to provide verisifcation fo the debt. Mortgage and/or its agents made false statements
5th rowI have problem with the bank checking account of my husband. He passed away in XXXX XXXX, XXXX. He has this account only under his name, and the montly bank statement only shows his name, but I am the beneficiary of his account. He oppened this bank account around XXXX or XXXX in Texas. Him always worked in differents state because he did XXXX work in XXXX. In XXXX XXXX I came from XXXX, but we married in XXXX. We moved together in diferents states but in XXXX, we came to XXXX and he add me as his beneficiary. He does n't have more children, only XXXX with me XXXX is XXXX years old and the other XXXX in XXXX years old. We visited the location of bank of America in XXXX XXXX XXXX. My husband explain to the worker than he need my name in the account only as a beneficiary. The worker from this bank added my name, I gave my personal documents, passport and social security number. But he never gave us any document to bring at home. That is comun in the bank they normaly do n't provide any document to the owner or to the beneficiary. The information only stay in the system in the bank. My husband was 100 % sure, because he knew if he passed away my name is in the system as a beneficiary. The worker say if he some day died, you do n't need to aprove with any court document only with his dead certificate. We never did something else like a will, because we did the same with other banks. When my husband passed away I went to the office of Bank of America XXXX XXXX XXXX XXXX, XXXX XXXX, XXXX ( XXXX ) XXXX XXXX/XXXX/XXXX around XXXX I did explain about my husband passed away XXXX of the ladies in the desk. She said, " You are the beneficiary of the occount. I can see your name. The only thing we need from you is the dead certificate of your husband account. Please call this number and you can fax the dead certificate. She gave me the phone number and I call the phone number of the bank of America, the same I visited, because they speak XXXX, but they connect the phone line with other company or department/ area of bank of america the name is XXXX XXXX XXXX this men told me he is the XXXX who works with the people when some XXXX XXXX, he has to register the information and I explain my situation and he said, to confirm your information give your full name and home address, and I gave this information by phone and he said you are the same I have in my sistem the only thing I need is your husband dead certificate, send me the fax to XXXX, I send this fax, I called to the phone number of bank of amercia XXXX to know if they have the dead certificate, but they said we do n't have it. I decided to visit the office of Bank of America in XXXX XXXX XXXX XXXX, XXXX XXXX, XXXX ( XXXX ) XXXX. The manager talk with me, I said I need to send this dead certificate by fax. She said I am no able to see your name as a beneficiary because your husband only have the account under his name and in the bank statement is only under his name. I said, I know that because benefciaries or PPO, do n't show the name in the bank statement. But I know i am the beneficiary, I spoke with XXXX employes before you and they said my name it is in the system, I said please give the name of the men, the XXXX I spoke XXXX, you have all my phone call in the system and you can see that, and she said his name is not here. Only I have the information about your call and I have the name of the men who help you with the traslation but I do n't have the other name. I felt so bad because I knew she was hiding me information. She call by phone some one else maybe XXXX of the employes XXXX, and the manager of the bank in XXXX XXXX said, you need too show me a document for the court which include you are the owner of your husband account. I said I do n't need that, because I am the beneficiary of my husband account.
ValueCountFrequency (%)
the 310784
 
4.4%
i 255992
 
3.6%
xxxx 254816
 
3.6%
to 238465
 
3.4%
and 186352
 
2.7%
a 145518
 
2.1%
135094
 
1.9%
my 133949
 
1.9%
of 123835
 
1.8%
that 109691
 
1.6%
Other values (45748) 5119022
73.0%
2023-11-05T00:57:28.449121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6984663
18.3%
e 3460475
 
9.1%
t 2672673
 
7.0%
a 2412921
 
6.3%
o 2066627
 
5.4%
n 2000059
 
5.2%
i 1725469
 
4.5%
r 1541400
 
4.0%
s 1514258
 
4.0%
h 1317870
 
3.5%
Other values (86) 12460163
32.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 27189775
71.3%
Space Separator 6984764
 
18.3%
Uppercase Letter 2603073
 
6.8%
Other Punctuation 795516
 
2.1%
Decimal Number 331963
 
0.9%
Close Punctuation 64953
 
0.2%
Open Punctuation 63148
 
0.2%
Control 55041
 
0.1%
Currency Symbol 40344
 
0.1%
Dash Punctuation 25844
 
0.1%
Other values (3) 2157
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3460475
12.7%
t 2672673
 
9.8%
a 2412921
 
8.9%
o 2066627
 
7.6%
n 2000059
 
7.4%
i 1725469
 
6.3%
r 1541400
 
5.7%
s 1514258
 
5.6%
h 1317870
 
4.8%
d 1237003
 
4.5%
Other values (16) 7241020
26.6%
Uppercase Letter
ValueCountFrequency (%)
X 1298674
49.9%
I 306889
 
11.8%
T 115414
 
4.4%
A 110510
 
4.2%
C 87501
 
3.4%
E 66235
 
2.5%
S 62990
 
2.4%
O 58693
 
2.3%
W 56531
 
2.2%
B 56324
 
2.2%
Other values (16) 383312
 
14.7%
Other Punctuation
ValueCountFrequency (%)
. 384124
48.3%
, 195565
24.6%
' 78558
 
9.9%
/ 71319
 
9.0%
" 17115
 
2.2%
! 10908
 
1.4%
: 8949
 
1.1%
? 8754
 
1.1%
; 5377
 
0.7%
% 5205
 
0.7%
Other values (5) 9642
 
1.2%
Decimal Number
ValueCountFrequency (%)
0 167217
50.4%
1 42151
 
12.7%
2 35497
 
10.7%
5 22261
 
6.7%
6 16552
 
5.0%
3 16275
 
4.9%
4 11513
 
3.5%
7 7544
 
2.3%
9 6835
 
2.1%
8 6118
 
1.8%
Math Symbol
ValueCountFrequency (%)
+ 696
44.8%
= 493
31.8%
~ 216
 
13.9%
> 72
 
4.6%
| 63
 
4.1%
< 12
 
0.8%
Open Punctuation
ValueCountFrequency (%)
{ 37287
59.0%
( 25419
40.3%
[ 442
 
0.7%
Close Punctuation
ValueCountFrequency (%)
} 37283
57.4%
) 27228
41.9%
] 442
 
0.7%
Space Separator
ValueCountFrequency (%)
6984663
> 99.9%
  101
 
< 0.1%
Control
ValueCountFrequency (%)
55041
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 40344
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 25844
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 551
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 54
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 29792848
78.1%
Common 8363730
 
21.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3460475
 
11.6%
t 2672673
 
9.0%
a 2412921
 
8.1%
o 2066627
 
6.9%
n 2000059
 
6.7%
i 1725469
 
5.8%
r 1541400
 
5.2%
s 1514258
 
5.1%
h 1317870
 
4.4%
X 1298674
 
4.4%
Other values (42) 9782422
32.8%
Common
ValueCountFrequency (%)
6984663
83.5%
. 384124
 
4.6%
, 195565
 
2.3%
0 167217
 
2.0%
' 78558
 
0.9%
/ 71319
 
0.9%
55041
 
0.7%
1 42151
 
0.5%
$ 40344
 
0.5%
{ 37287
 
0.4%
Other values (34) 307461
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38156477
> 99.9%
None 101
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6984663
18.3%
e 3460475
 
9.1%
t 2672673
 
7.0%
a 2412921
 
6.3%
o 2066627
 
5.4%
n 2000059
 
5.2%
i 1725469
 
4.5%
r 1541400
 
4.0%
s 1514258
 
4.0%
h 1317870
 
3.5%
Other values (85) 12460062
32.7%
None
ValueCountFrequency (%)
  101
100.0%
Distinct8
Distinct (%)< 0.1%
Missing148802
Missing (%)71.8%
Memory size1.6 MiB
2023-11-05T00:57:28.900362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length119
Median length95
Mean length76.85887304
Min length48

Characters and Unicode

Total characters4493016
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowCompany has responded to the consumer and the CFPB and chooses not to provide a public response
2nd rowCompany chooses not to provide a public response
3rd rowCompany chooses not to provide a public response
4th rowCompany has responded to the consumer and the CFPB and chooses not to provide a public response
5th rowCompany has responded to the consumer and the CFPB and chooses not to provide a public response
ValueCountFrequency (%)
to 94253
11.9%
the 71725
9.1%
and 71716
9.1%
company 58461
7.4%
a 58395
7.4%
provide 58393
7.4%
response 58393
7.4%
public 58393
7.4%
not 58393
7.4%
chooses 58393
7.4%
Other values (41) 144085
18.2%
2023-11-05T00:57:29.887420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
732142
16.3%
o 516671
11.5%
e 413269
 
9.2%
s 341292
 
7.6%
n 318767
 
7.1%
p 269694
 
6.0%
a 224860
 
5.0%
t 224756
 
5.0%
d 201959
 
4.5%
r 188828
 
4.2%
Other values (17) 1060778
23.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3558984
79.2%
Space Separator 732142
 
16.3%
Uppercase Letter 201890
 
4.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 516671
14.5%
e 413269
11.6%
s 341292
9.6%
n 318767
9.0%
p 269694
 
7.6%
a 224860
 
6.3%
t 224756
 
6.3%
d 201959
 
5.7%
r 188828
 
5.3%
h 166037
 
4.7%
Other values (12) 692851
19.5%
Uppercase Letter
ValueCountFrequency (%)
C 94316
46.7%
F 35858
 
17.8%
P 35858
 
17.8%
B 35858
 
17.8%
Space Separator
ValueCountFrequency (%)
732142
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3760874
83.7%
Common 732142
 
16.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 516671
13.7%
e 413269
11.0%
s 341292
 
9.1%
n 318767
 
8.5%
p 269694
 
7.2%
a 224860
 
6.0%
t 224756
 
6.0%
d 201959
 
5.4%
r 188828
 
5.0%
h 166037
 
4.4%
Other values (16) 894741
23.8%
Common
ValueCountFrequency (%)
732142
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4493016
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
732142
16.3%
o 516671
11.5%
e 413269
 
9.2%
s 341292
 
7.6%
n 318767
 
7.1%
p 269694
 
6.0%
a 224860
 
5.0%
t 224756
 
5.0%
d 201959
 
4.5%
r 188828
 
4.2%
Other values (17) 1060778
23.6%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
2023-11-05T00:57:30.297091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length21
Mean length24.15883914
Min length12

Characters and Unicode

Total characters5007161
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCITIBANK, N.A.
2nd rowCITIBANK, N.A.
3rd rowU.S. BANCORP
4th rowJPMORGAN CHASE & CO.
5th rowBANK OF AMERICA, NATIONAL ASSOCIATION
ValueCountFrequency (%)
95281
11.9%
bank 65440
 
8.2%
of 65440
 
8.2%
america 65440
 
8.2%
national 65440
 
8.2%
association 65440
 
8.2%
wells 53111
 
6.6%
fargo 53111
 
6.6%
company 53111
 
6.6%
jpmorgan 42170
 
5.3%
Other values (6) 177418
22.1%
2023-11-05T00:57:31.160429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 729522
14.6%
594142
11.9%
O 464520
 
9.3%
N 437921
 
8.7%
I 330442
 
6.6%
C 314870
 
6.3%
S 238359
 
4.8%
R 172919
 
3.5%
L 171662
 
3.4%
T 165221
 
3.3%
Other values (15) 1387583
27.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4082709
81.5%
Space Separator 594142
 
11.9%
Other Punctuation 330310
 
6.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 729522
17.9%
O 464520
11.4%
N 437921
10.7%
I 330442
 
8.1%
C 314870
 
7.7%
S 238359
 
5.8%
R 172919
 
4.2%
L 171662
 
4.2%
T 165221
 
4.0%
M 160721
 
3.9%
Other values (11) 896552
22.0%
Other Punctuation
ValueCountFrequency (%)
. 135248
40.9%
, 99781
30.2%
& 95281
28.8%
Space Separator
ValueCountFrequency (%)
594142
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4082709
81.5%
Common 924452
 
18.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 729522
17.9%
O 464520
11.4%
N 437921
10.7%
I 330442
 
8.1%
C 314870
 
7.7%
S 238359
 
5.8%
R 172919
 
4.2%
L 171662
 
4.2%
T 165221
 
4.0%
M 160721
 
3.9%
Other values (11) 896552
22.0%
Common
ValueCountFrequency (%)
594142
64.3%
. 135248
 
14.6%
, 99781
 
10.8%
& 95281
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5007161
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 729522
14.6%
594142
11.9%
O 464520
 
9.3%
N 437921
 
8.7%
I 330442
 
6.6%
C 314870
 
6.3%
S 238359
 
4.8%
R 172919
 
3.5%
L 171662
 
3.4%
T 165221
 
3.3%
Other values (15) 1387583
27.7%

State
Text

MISSING 

Distinct62
Distinct (%)< 0.1%
Missing2194
Missing (%)1.1%
Memory size1.6 MiB
2023-11-05T00:57:31.597797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters410132
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPA
2nd rowPA
3rd rowVA
4th rowGA
5th rowGA
ValueCountFrequency (%)
ca 37505
18.3%
fl 20134
 
9.8%
ny 15616
 
7.6%
tx 12123
 
5.9%
nj 9242
 
4.5%
ga 9054
 
4.4%
il 7482
 
3.6%
md 6400
 
3.1%
pa 6303
 
3.1%
va 5947
 
2.9%
Other values (52) 75260
36.7%
2023-11-05T00:57:32.477001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 77827
19.0%
C 53041
12.9%
N 43330
10.6%
L 30522
 
7.4%
M 23567
 
5.7%
F 20151
 
4.9%
I 19329
 
4.7%
T 18765
 
4.6%
Y 16891
 
4.1%
O 15426
 
3.8%
Other values (14) 91283
22.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 410132
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 77827
19.0%
C 53041
12.9%
N 43330
10.6%
L 30522
 
7.4%
M 23567
 
5.7%
F 20151
 
4.9%
I 19329
 
4.7%
T 18765
 
4.6%
Y 16891
 
4.1%
O 15426
 
3.8%
Other values (14) 91283
22.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 410132
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 77827
19.0%
C 53041
12.9%
N 43330
10.6%
L 30522
 
7.4%
M 23567
 
5.7%
F 20151
 
4.9%
I 19329
 
4.7%
T 18765
 
4.6%
Y 16891
 
4.1%
O 15426
 
3.8%
Other values (14) 91283
22.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 410132
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 77827
19.0%
C 53041
12.9%
N 43330
10.6%
L 30522
 
7.4%
M 23567
 
5.7%
F 20151
 
4.9%
I 19329
 
4.7%
T 18765
 
4.6%
Y 16891
 
4.1%
O 15426
 
3.8%
Other values (14) 91283
22.3%

ZIP code
Text

MISSING 

Distinct12612
Distinct (%)6.4%
Missing9286
Missing (%)4.5%
Memory size1.6 MiB
2023-11-05T00:57:33.271267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters989870
Distinct characters16
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3795 ?
Unique (%)1.9%

Sample

1st row151XX
2nd row152XX
3rd row22406
4th row30044
5th row30044
ValueCountFrequency (%)
945xx 613
 
0.3%
070xx 601
 
0.3%
117xx 555
 
0.3%
900xx 513
 
0.3%
331xx 510
 
0.3%
334xx 497
 
0.3%
300xx 469
 
0.2%
100xx 455
 
0.2%
080xx 449
 
0.2%
750xx 446
 
0.2%
Other values (12602) 192866
97.4%
2023-11-05T00:57:34.691444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 138010
13.9%
X 124148
12.5%
1 107470
10.9%
3 103821
10.5%
2 99567
10.1%
9 80117
8.1%
4 72743
7.3%
7 70831
7.2%
5 67349
6.8%
8 65029
6.6%
Other values (6) 60785
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 865700
87.5%
Uppercase Letter 124148
 
12.5%
Dash Punctuation 17
 
< 0.1%
Other Punctuation 4
 
< 0.1%
Modifier Symbol 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 138010
15.9%
1 107470
12.4%
3 103821
12.0%
2 99567
11.5%
9 80117
9.3%
4 72743
8.4%
7 70831
8.2%
5 67349
7.8%
8 65029
7.5%
6 60763
7.0%
Other Punctuation
ValueCountFrequency (%)
* 2
50.0%
. 1
25.0%
! 1
25.0%
Uppercase Letter
ValueCountFrequency (%)
X 124148
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 17
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 865722
87.5%
Latin 124148
 
12.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 138010
15.9%
1 107470
12.4%
3 103821
12.0%
2 99567
11.5%
9 80117
9.3%
4 72743
8.4%
7 70831
8.2%
5 67349
7.8%
8 65029
7.5%
6 60763
7.0%
Other values (5) 22
 
< 0.1%
Latin
ValueCountFrequency (%)
X 124148
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 989870
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 138010
13.9%
X 124148
12.5%
1 107470
10.9%
3 103821
10.5%
2 99567
10.1%
9 80117
8.1%
4 72743
7.3%
7 70831
7.2%
5 67349
6.8%
8 65029
6.6%
Other values (6) 60785
6.1%

Tags
Text

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing178995
Missing (%)86.4%
Memory size1.6 MiB
2023-11-05T00:57:35.105887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length14
Mean length15.00367946
Min length13

Characters and Unicode

Total characters424079
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOlder American
2nd rowServicemember
3rd rowOlder American
4th rowServicemember
5th rowOlder American
ValueCountFrequency (%)
older 20934
40.6%
american 20934
40.6%
servicemember 9711
18.8%
2023-11-05T00:57:36.006221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 80712
19.0%
r 61290
14.5%
m 40356
9.5%
i 30645
 
7.2%
c 30645
 
7.2%
23314
 
5.5%
O 20934
 
4.9%
l 20934
 
4.9%
d 20934
 
4.9%
A 20934
 
4.9%
Other values (6) 73381
17.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 346806
81.8%
Uppercase Letter 51579
 
12.2%
Space Separator 23314
 
5.5%
Other Punctuation 2380
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 80712
23.3%
r 61290
17.7%
m 40356
11.6%
i 30645
 
8.8%
c 30645
 
8.8%
l 20934
 
6.0%
d 20934
 
6.0%
a 20934
 
6.0%
n 20934
 
6.0%
v 9711
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
O 20934
40.6%
A 20934
40.6%
S 9711
18.8%
Space Separator
ValueCountFrequency (%)
23314
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2380
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 398385
93.9%
Common 25694
 
6.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 80712
20.3%
r 61290
15.4%
m 40356
10.1%
i 30645
 
7.7%
c 30645
 
7.7%
O 20934
 
5.3%
l 20934
 
5.3%
d 20934
 
5.3%
A 20934
 
5.3%
a 20934
 
5.3%
Other values (4) 50067
12.6%
Common
ValueCountFrequency (%)
23314
90.7%
, 2380
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 424079
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 80712
19.0%
r 61290
14.5%
m 40356
9.5%
i 30645
 
7.2%
c 30645
 
7.2%
23314
 
5.5%
O 20934
 
4.9%
l 20934
 
4.9%
d 20934
 
4.9%
A 20934
 
4.9%
Other values (6) 73381
17.3%
Distinct4
Distinct (%)< 0.1%
Missing155947
Missing (%)75.2%
Memory size1.6 MiB
2023-11-05T00:57:36.387881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length16
Mean length17.06789702
Min length5

Characters and Unicode

Total characters875805
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowConsent provided
2nd rowConsent provided
3rd rowConsent provided
4th rowConsent provided
5th rowConsent provided
ValueCountFrequency (%)
consent 49121
40.9%
provided 49120
40.9%
not 19727
16.4%
other 2192
 
1.8%
withdrawn 1
 
< 0.1%
2023-11-05T00:57:37.313237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 117970
13.5%
o 117968
13.5%
e 100433
11.5%
d 98241
11.2%
t 71041
8.1%
68848
7.9%
r 51313
5.9%
C 49121
5.6%
s 49121
5.6%
i 49121
5.6%
Other values (6) 102628
11.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 755644
86.3%
Space Separator 68848
 
7.9%
Uppercase Letter 51313
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 117970
15.6%
o 117968
15.6%
e 100433
13.3%
d 98241
13.0%
t 71041
9.4%
r 51313
6.8%
s 49121
6.5%
i 49121
6.5%
p 49120
6.5%
v 49120
6.5%
Other values (3) 2196
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
C 49121
95.7%
O 2192
 
4.3%
Space Separator
ValueCountFrequency (%)
68848
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 806957
92.1%
Common 68848
 
7.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 117970
14.6%
o 117968
14.6%
e 100433
12.4%
d 98241
12.2%
t 71041
8.8%
r 51313
6.4%
C 49121
6.1%
s 49121
6.1%
i 49121
6.1%
p 49120
6.1%
Other values (5) 53508
6.6%
Common
ValueCountFrequency (%)
68848
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 875805
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 117970
13.5%
o 117968
13.5%
e 100433
11.5%
d 98241
11.2%
t 71041
8.1%
68848
7.9%
r 51313
5.9%
C 49121
5.6%
s 49121
5.6%
i 49121
5.6%
Other values (6) 102628
11.7%
Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
2023-11-05T00:57:37.675776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length3
Mean length5.05120139
Min length3

Characters and Unicode

Total characters1046912
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWeb
2nd rowWeb
3rd rowWeb
4th rowPhone
5th rowReferral
ValueCountFrequency (%)
web 115010
52.6%
referral 59558
27.2%
phone 17530
 
8.0%
postal 11506
 
5.3%
mail 11506
 
5.3%
fax 3539
 
1.6%
email 117
 
0.1%
2023-11-05T00:57:38.537559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 251656
24.0%
r 119116
11.4%
W 115010
11.0%
b 115010
11.0%
a 86226
 
8.2%
l 82687
 
7.9%
R 59558
 
5.7%
f 59558
 
5.7%
o 29036
 
2.8%
P 29036
 
2.8%
Other values (10) 100019
 
9.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 828146
79.1%
Uppercase Letter 207260
 
19.8%
Space Separator 11506
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 251656
30.4%
r 119116
14.4%
b 115010
13.9%
a 86226
 
10.4%
l 82687
 
10.0%
f 59558
 
7.2%
o 29036
 
3.5%
h 17530
 
2.1%
n 17530
 
2.1%
m 11623
 
1.4%
Other values (4) 38174
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
W 115010
55.5%
R 59558
28.7%
P 29036
 
14.0%
F 3539
 
1.7%
E 117
 
0.1%
Space Separator
ValueCountFrequency (%)
11506
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1035406
98.9%
Common 11506
 
1.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 251656
24.3%
r 119116
11.5%
W 115010
11.1%
b 115010
11.1%
a 86226
 
8.3%
l 82687
 
8.0%
R 59558
 
5.8%
f 59558
 
5.8%
o 29036
 
2.8%
P 29036
 
2.8%
Other values (9) 88513
 
8.5%
Common
ValueCountFrequency (%)
11506
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1046912
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 251656
24.0%
r 119116
11.4%
W 115010
11.0%
b 115010
11.0%
a 86226
 
8.2%
l 82687
 
7.9%
R 59558
 
5.7%
f 59558
 
5.7%
o 29036
 
2.8%
P 29036
 
2.8%
Other values (10) 100019
 
9.6%
Distinct1898
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
2023-11-05T00:57:39.191112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters2072600
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)< 0.1%

Sample

1st row2016-10-29
2nd row2015-03-27
3rd row2015-04-22
4th row2013-04-30
5th row2013-05-31
ValueCountFrequency (%)
2012-06-21 460
 
0.2%
2012-06-15 359
 
0.2%
2013-08-22 308
 
0.1%
2013-01-17 303
 
0.1%
2014-02-07 297
 
0.1%
2013-09-05 297
 
0.1%
2012-06-04 295
 
0.1%
2013-01-14 284
 
0.1%
2012-05-29 281
 
0.1%
2013-01-22 279
 
0.1%
Other values (1888) 204097
98.5%
2023-11-05T00:57:40.295820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 466880
22.5%
- 414520
20.0%
1 380602
18.4%
2 366895
17.7%
3 94636
 
4.6%
6 79485
 
3.8%
5 73430
 
3.5%
4 72681
 
3.5%
7 47532
 
2.3%
9 38010
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1658080
80.0%
Dash Punctuation 414520
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 466880
28.2%
1 380602
23.0%
2 366895
22.1%
3 94636
 
5.7%
6 79485
 
4.8%
5 73430
 
4.4%
4 72681
 
4.4%
7 47532
 
2.9%
9 38010
 
2.3%
8 37929
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
- 414520
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2072600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 466880
22.5%
- 414520
20.0%
1 380602
18.4%
2 366895
17.7%
3 94636
 
4.6%
6 79485
 
3.8%
5 73430
 
3.5%
4 72681
 
3.5%
7 47532
 
2.3%
9 38010
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2072600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 466880
22.5%
- 414520
20.0%
1 380602
18.4%
2 366895
17.7%
3 94636
 
4.6%
6 79485
 
3.8%
5 73430
 
3.5%
4 72681
 
3.5%
7 47532
 
2.3%
9 38010
 
1.8%
Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
2023-11-05T00:57:40.720701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length23
Mean length23.80850622
Min length6

Characters and Unicode

Total characters4934551
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowClosed with explanation
2nd rowClosed with explanation
3rd rowClosed with monetary relief
4th rowClosed with explanation
5th rowClosed with explanation
ValueCountFrequency (%)
closed 207085
31.4%
with 193692
29.4%
explanation 148930
22.6%
relief 55881
 
8.5%
monetary 22918
 
3.5%
non-monetary 19005
 
2.9%
without 11119
 
1.7%
untimely 175
 
< 0.1%
response 175
 
< 0.1%
2023-11-05T00:57:41.612645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 510225
10.3%
451720
 
9.2%
o 428237
 
8.7%
l 412071
 
8.4%
i 409797
 
8.3%
t 406958
 
8.2%
n 378143
 
7.7%
a 339783
 
6.9%
s 207435
 
4.2%
C 207085
 
4.2%
Other values (12) 1183097
24.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4256566
86.3%
Space Separator 451720
 
9.2%
Uppercase Letter 207260
 
4.2%
Dash Punctuation 19005
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 510225
12.0%
o 428237
10.1%
l 412071
9.7%
i 409797
9.6%
t 406958
9.6%
n 378143
8.9%
a 339783
8.0%
s 207435
 
4.9%
d 207085
 
4.9%
w 204811
 
4.8%
Other values (8) 752021
17.7%
Uppercase Letter
ValueCountFrequency (%)
C 207085
99.9%
U 175
 
0.1%
Space Separator
ValueCountFrequency (%)
451720
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19005
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4463826
90.5%
Common 470725
 
9.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 510225
11.4%
o 428237
9.6%
l 412071
9.2%
i 409797
9.2%
t 406958
9.1%
n 378143
 
8.5%
a 339783
 
7.6%
s 207435
 
4.6%
C 207085
 
4.6%
d 207085
 
4.6%
Other values (10) 957007
21.4%
Common
ValueCountFrequency (%)
451720
96.0%
- 19005
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4934551
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 510225
10.3%
451720
 
9.2%
o 428237
 
8.7%
l 412071
 
8.4%
i 409797
 
8.3%
t 406958
 
8.2%
n 378143
 
7.7%
a 339783
 
6.9%
s 207435
 
4.2%
C 207085
 
4.2%
Other values (12) 1183097
24.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
2023-11-05T00:57:41.965722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.977530638
Min length2

Characters and Unicode

Total characters617123
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes
2nd rowYes
3rd rowYes
4th rowYes
5th rowYes
ValueCountFrequency (%)
yes 202603
97.8%
no 4657
 
2.2%
2023-11-05T00:57:42.744691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
Y 202603
32.8%
e 202603
32.8%
s 202603
32.8%
N 4657
 
0.8%
o 4657
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 409863
66.4%
Uppercase Letter 207260
33.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 202603
49.4%
s 202603
49.4%
o 4657
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
Y 202603
97.8%
N 4657
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 617123
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
Y 202603
32.8%
e 202603
32.8%
s 202603
32.8%
N 4657
 
0.8%
o 4657
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 617123
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Y 202603
32.8%
e 202603
32.8%
s 202603
32.8%
N 4657
 
0.8%
o 4657
 
0.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
2023-11-05T00:57:43.038175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.216650584
Min length2

Characters and Unicode

Total characters459423
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowYes
5th rowNo
ValueCountFrequency (%)
no 162357
78.3%
yes 44903
 
21.7%
2023-11-05T00:57:43.850005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 162357
35.3%
o 162357
35.3%
Y 44903
 
9.8%
e 44903
 
9.8%
s 44903
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 252163
54.9%
Uppercase Letter 207260
45.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 162357
64.4%
e 44903
 
17.8%
s 44903
 
17.8%
Uppercase Letter
ValueCountFrequency (%)
N 162357
78.3%
Y 44903
 
21.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 459423
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 162357
35.3%
o 162357
35.3%
Y 44903
 
9.8%
e 44903
 
9.8%
s 44903
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 459423
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 162357
35.3%
o 162357
35.3%
Y 44903
 
9.8%
e 44903
 
9.8%
s 44903
 
9.8%

Complaint ID
Real number (ℝ)

UNIQUE 

Distinct207260
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1028618.835
Minimum22
Maximum2412707
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2023-11-05T00:57:44.393028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile54355.5
Q1345621.75
median920972
Q31710703.75
95-th percentile2264221.95
Maximum2412707
Range2412685
Interquartile range (IQR)1365082

Descriptive statistics

Standard deviation753334.843
Coefficient of variation (CV)0.7323751203
Kurtosis-1.309556439
Mean1028618.835
Median Absolute Deviation (MAD)672060
Skewness0.273029066
Sum2.131915397 × 1011
Variance5.675133856 × 1011
MonotonicityNot monotonic
2023-11-05T00:57:45.026246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2180490 1
 
< 0.1%
1544136 1
 
< 0.1%
805211 1
 
< 0.1%
124159 1
 
< 0.1%
2366997 1
 
< 0.1%
11359 1
 
< 0.1%
316129 1
 
< 0.1%
198807 1
 
< 0.1%
24552 1
 
< 0.1%
982444 1
 
< 0.1%
Other values (207250) 207250
> 99.9%
ValueCountFrequency (%)
22 1
< 0.1%
24 1
< 0.1%
26 1
< 0.1%
36 1
< 0.1%
39 1
< 0.1%
ValueCountFrequency (%)
2412707 1
< 0.1%
2412680 1
< 0.1%
2412669 1
< 0.1%
2412644 1
< 0.1%
2412589 1
< 0.1%